Disclaimer

The following notes draw heavily from the class lectures and Rmd file covering Plotly. For the original material and appropriate credit please see the class Lectures and source code.

Some function notes also come directly from the official R Documentation files.

What is Plotly?

Plotly is a web application for creating and sharing data visualizations. Most importantly, the data plots it creates are interactive.

Plotly can work with several programming languages and applications including R, Python, and Microsoft Excel.

Plotly for R

install.packages("plotly")
library(plotly)

An extensive reference for using plotly in R can be found at https://plot.ly/r/reference/.

The most basic function is plot_ly(), with the simple format of p <- plot_ly(data = df, ~x = x column name, ~y = y column name, type = type of plot). However, this varies depending on the type and plot_ly() will try to create a sensible plot based on the information you give it, inferring as needed.

A simple scatterplot

library(plotly)
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter")

A more complicated scatterplot

By specifying the color and size arguments you can make each point in your scatterplot a different color and size.

plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", 
        color = ~factor(cyl), size = ~hp)

Time series (a plotly aside)

Time series objects are a special dataset type in which multiple observations (i.e. each row of data) are sampled at equispaced points in time. Being such a common and useful type, R has a collection of functions to work with time series. The function ts() is used to create a time series -ts- object from a data vector x. Additional key arguments are start, end, and frequency (respectively, the time of the first observation, last observation, and sampling frequency within the natural time period.)

The function tsp() returns the key attributes (in a sense, the meta data) of a ts object.

The functions start() and end() return the time of the first and last observation. Both return a vector length 2, with the first value being the time period, and the second being the fraction of the time period.

The function time() creates a vector of times at which a series was sampled, from the tsp() attributes.

The functions cycle(), frequency(), and deltat() return the positions in the cycle of each observation, the number of samples per unit time, and the time interval between observations, respectively.

As a quick example, we can use the data(airmiles) data set, which is the revenue passenger miles flown by US airlines for each year from 1937 to 1960:

class(airmiles)
[1] "ts"
tsp(airmiles)
[1] 1937 1960    1
start(airmiles)
[1] 1937    1
head(airmiles)
[1]  412  480  683 1052 1385 1418
head(time(airmiles))
[1] 1937 1938 1939 1940 1941 1942

Line graphs are the default graph for plot_ly(). They’re of course useful for showing change over time:

plot_ly(x = time(airmiles), y = airmiles, type = "scatter", mode = "lines")

A list of plotly graph types

The following is a partial list of the type = "..." or type = '...' argument allowed by plot_ly().
scatter
histogram
box
chloropleth
heatmap
surface
scatter3d

The last two in the list are 3D plots.

Also keep in mind that the set of required arguments can change - and be a lot - depending on the type selected.

Multi-line plots

To draw multiple lines on the same graph, plotly requires the data to be a dataframe and organized in long and narrow format. In other words, all the y data must be in one column, all the x data must be in a second column, and there must be a third column acting like a single factor variable for the set of {x, y}). The latter is passed to the color = argument in plot_ly().

A recommended way to accomplish the required format is by using functions like gather() and mutate() in the tidyr and dplyr packages. They are “must use” packages for manipulating data sets. Here’s an example:

library(plotly)
library(tidyr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.1
data("EuStockMarkets")
stocks <- as.data.frame(EuStockMarkets) %>%
  gather(index, price) %>%
  mutate(time = rep(time(EuStockMarkets), 4))
plot_ly(stocks, x = ~time, y = ~price, color = ~index, type = "scatter", mode = "lines")

Heatmaps

Create a heatmap by using the type = "heatmap" argument.

terrain1 <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
plot_ly(z = ~terrain1, type = "heatmap")

More plotly resources